"""simple docstring"""







import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE             ={
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

__SCREAMING_SNAKE_CASE             ={
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

__SCREAMING_SNAKE_CASE             ={
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any ):
     lowercase_      :							int						=      set()
     lowercase_      :							Dict						=      word[0]
     for char in word[1:]:
          pairs.add((prev_char, char) )
          lowercase_      :							List[str]						=      char

     lowercase_      :							Any						=      set(UpperCAmelCase_ )
     return pairs







class 			UpperCamelCase						(     _lowerCAmelCase    ):
   lowercase				=    VOCAB_FILES_NAMES
   lowercase				=    PRETRAINED_VOCAB_FILES_MAP
   lowercase				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   def __init__(     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase="<s>"	,__UpperCamelCase="</s>"	,__UpperCamelCase="</s>"	,__UpperCamelCase="<s>"	,__UpperCamelCase="<unk>"	,__UpperCamelCase="<pad>"	,__UpperCamelCase="<mask>"	,**__UpperCamelCase	,)      -> Union[str, Any]:


        '''simple docstring'''


        super().__init__(
            bos_token=UpperCAmelCase_	,eos_token=UpperCAmelCase_	,unk_token=UpperCAmelCase_	,sep_token=UpperCAmelCase_	,cls_token=UpperCAmelCase_	,pad_token=UpperCAmelCase_	,mask_token=UpperCAmelCase_	,**UpperCAmelCase_	,)

        lowercase_      :							str						=      vocab_file
        lowercase_      :							Tuple						=      merges_file

        lowercase_      :							List[Any]						=      {}
        lowercase_      :							List[str]						=      0
        lowercase_      :							Any						=      1
        lowercase_      :							str						=      2
        lowercase_      :							str						=      3

        self.add_from_file(UpperCAmelCase_     )

        lowercase_      :							Dict						=      {v: k for k, v in self.encoder.items()}

        with open(UpperCAmelCase_	,encoding='utf-8'     ) as merges_handle:
             lowercase_      :							Any						=      merges_handle.read().split('\n'     )[:-1]
        lowercase_      :							Dict						=      [tuple(merge.split()[:-1]     ) for merge in merges]
        lowercase_      :							Union[str, Any]						=      dict(zip(UpperCAmelCase_	,range(len(UpperCAmelCase_     )     )     )     )
        lowercase_      :							int						=      {}
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None     )      -> List[int]:


        '''simple docstring'''


        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase_      :							List[str]						=      [self.cls_token_id]
        lowercase_      :							List[str]						=      [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None	,__UpperCamelCase = False     )      -> List[int]:


        '''simple docstring'''


        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=UpperCAmelCase_	,token_ids_a=UpperCAmelCase_	,already_has_special_tokens=UpperCAmelCase_     )

        if token_ids_a is None:
             return [1] + ([0] * len(UpperCAmelCase_     )) + [1]
        return [1] + ([0] * len(UpperCAmelCase_     )) + [1, 1] + ([0] * len(UpperCAmelCase_     )) + [1]
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None     )      -> List[int]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      [self.sep_token_id]
        lowercase_      :							Optional[int]						=      [self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep     ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
   @property
   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        return len(self.encoder     )
   def      _UpperCAmelCase  (     self     )      -> Tuple:


        '''simple docstring'''


        return dict(self.encoder	,**self.added_tokens_encoder     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        if token in self.cache:
             return self.cache[token]
        lowercase_      :							Optional[int]						=      tuple(UpperCAmelCase_     )
        lowercase_      :							List[str]						=      tuple(list(word[:-1]     ) + [word[-1] + '</w>']     )
        lowercase_      :							Optional[Any]						=      get_pairs(UpperCAmelCase_     )

        if not pairs:
             return token

        while True:
             lowercase_      :							Tuple						=      min(UpperCAmelCase_	,key=lambda __UpperCamelCase     : self.bpe_ranks.get(UpperCAmelCase_	,float('inf'     )     )     )
             if bigram not in self.bpe_ranks:
                  break
             lowercase_				,    lowercase_      :							Tuple						=      bigram
             lowercase_      :							List[str]						=      []
             lowercase_      :							Tuple						=      0
             while i < len(UpperCAmelCase_     ):
                  try:
                       lowercase_      :							Optional[Any]						=      word.index(UpperCAmelCase_	,UpperCAmelCase_     )
                  except ValueError:
                       new_word.extend(word[i:]     )
                       break
                  else:
                       new_word.extend(word[i:j]     )
                       lowercase_      :							List[Any]						=      j

                  if word[i] == first and i < len(UpperCAmelCase_     ) - 1 and word[i + 1] == second:
                       new_word.append(first + second     )
                       i += 2
                  else:
                       new_word.append(word[i]     )
                       i += 1
             lowercase_      :							Union[str, Any]						=      tuple(UpperCAmelCase_     )
             lowercase_      :							Optional[int]						=      new_word
             if len(UpperCAmelCase_     ) == 1:
                  break
             else:
                  lowercase_      :							List[str]						=      get_pairs(UpperCAmelCase_     )
        lowercase_      :							Tuple						=      '@@ '.join(UpperCAmelCase_     )
        lowercase_      :							Tuple						=      word[:-4]
        lowercase_      :							Dict						=      word
        return word
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> int:


        '''simple docstring'''


        lowercase_      :							Tuple						=      []

        lowercase_      :							Any						=      re.findall(r'\S+\n?'	,UpperCAmelCase_     )

        for token in words:
             split_tokens.extend(list(self.bpe(UpperCAmelCase_     ).split(' '     )     )     )
        return split_tokens
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> str:


        '''simple docstring'''


        return self.encoder.get(UpperCAmelCase_	,self.encoder.get(self.unk_token     )     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> Union[str, Any]:


        '''simple docstring'''


        return self.decoder.get(UpperCAmelCase_	,self.unk_token     )
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							Union[str, Any]						=      ' '.join(UpperCAmelCase_     ).replace('@@ '	,''     ).strip()
        return out_string
   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase = None     )      -> Tuple[str]:


        '''simple docstring'''


        if not os.path.isdir(UpperCAmelCase_     ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
             return
        lowercase_      :							Any						=      os.path.join(
            UpperCAmelCase_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
        lowercase_      :							Tuple						=      os.path.join(
            UpperCAmelCase_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCAmelCase_     ):
             copyfile(self.vocab_file	,UpperCAmelCase_     )

        if os.path.abspath(self.merges_file     ) != os.path.abspath(UpperCAmelCase_     ):
             copyfile(self.merges_file	,UpperCAmelCase_     )

        return out_vocab_file, out_merge_file







   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> Optional[int]:


        '''simple docstring'''


        if isinstance(UpperCAmelCase_	,UpperCAmelCase_     ):
             try:
                  with open(UpperCAmelCase_	,'r'	,encoding='utf-8'     ) as fd:
                       self.add_from_file(UpperCAmelCase_     )
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''     )
             return

        lowercase_      :							Dict						=      f.readlines()
        for lineTmp in lines:
             lowercase_      :							Tuple						=      lineTmp.strip()
             lowercase_      :							str						=      line.rfind(' '     )
             if idx == -1:
                  raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\''     )
             lowercase_      :							int						=      line[:idx]
             lowercase_      :							Optional[int]						=      len(self.encoder     )



"""simple docstring"""


import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel



class 	__a	:






       def __init__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				str=13							,   UpperCAmelCase_			:				Optional[int]=7							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				Optional[Any]=True							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				int=True							,   UpperCAmelCase_			:				Dict=99							,   UpperCAmelCase_			:				str=32							,   UpperCAmelCase_			:				List[Any]=5							,   UpperCAmelCase_			:				str=4							,   UpperCAmelCase_			:				Union[str, Any]=4							,   UpperCAmelCase_			:				Optional[int]="gelu"							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				Any=0.1							,   UpperCAmelCase_			:				int=True							,   UpperCAmelCase_			:				Union[str, Any]=512							,   UpperCAmelCase_			:				int=16							,   UpperCAmelCase_			:				Optional[Any]=2							,   UpperCAmelCase_			:				str=0.02							,   UpperCAmelCase_			:				Any=3							,   UpperCAmelCase_			:				Any=4							,   UpperCAmelCase_			:				Optional[Any]=None							,   )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		seq_length
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_input_mask
              UpperCamelCase       			=		use_token_type_ids
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_multiple_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout
              UpperCamelCase       			=		attention_dropout
              UpperCamelCase       			=		weight_tying
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		type_sequence_label_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		num_labels
              UpperCamelCase       			=		num_choices
              UpperCamelCase       			=		scope






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size      )

              UpperCamelCase       			=		None
              if self.use_input_mask:
                     UpperCamelCase       			=		random_attention_mask([self.batch_size, self.seq_length]      )

              UpperCamelCase       			=		None
              if self.use_labels:
                     UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.num_labels      )

              UpperCamelCase       			=		self.get_config()

              return config, input_ids, input_mask, token_labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Dict:







              """simple docstring"""
              return GPTNeoXJapaneseConfig(
                  vocab_size=self.vocab_size							,   hidden_size=self.hidden_size							,   num_hidden_layers=self.num_hidden_layers							,   num_attention_heads=self.num_attention_heads							,   intermediate_multiple_size=self.intermediate_multiple_size							,   hidden_act=self.hidden_act							,   hidden_dropout=self.hidden_dropout							,   attention_dropout=self.attention_dropout							,   weight_tying=self.weight_tying							,   max_position_embeddings=self.max_position_embeddings							,   type_vocab_size=self.type_vocab_size							,   is_decoder=UpperCAmelCase_							,   initializer_range=self.initializer_range							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.prepare_config_and_inputs()

              UpperCamelCase       			=		True

              return config, input_ids, input_mask, token_labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseModel(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				List[str]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		True
              UpperCamelCase       			=		GPTNeoXJapaneseModel(UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )
              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   labels=UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.vocab_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		True
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()

              # first forward pass
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )
              UpperCamelCase       			=		outputs.past_key_values

              # create hypothetical multiple next token and extent to next_input_ids
              UpperCamelCase       			=		ids_tensor((self.batch_size, 3)							,   config.vocab_size      )
              UpperCamelCase       			=		ids_tensor((self.batch_size, 3)							,   vocab_size=2      )

              # append to next input_ids and
              UpperCamelCase       			=		torch.cat([input_ids, next_tokens]							,   dim=-1      )
              UpperCamelCase       			=		torch.cat([input_mask, next_mask]							,   dim=-1      )

              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_      )
              UpperCamelCase       			=		output_from_no_past["hidden_states"][0]
              UpperCamelCase       			=		model(
                  UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   past_key_values=UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_							,   )["hidden_states"][0]

              # select random slice
              UpperCamelCase       			=		ids_tensor((1,)							,   output_from_past.shape[-1]      ).item()
              UpperCamelCase       			=		output_from_no_past[:, -3:, random_slice_idx].detach()
              UpperCamelCase       			=		output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		config_and_inputs
              UpperCamelCase       			=		{"input_ids": input_ids, "attention_mask": input_mask}
              return config, inputs_dict





@require_torch
class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Union[str, Any]				=      (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       UpperCamelCase_		:       Optional[int]				=      (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       UpperCamelCase_		:       List[str]				=      (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       UpperCamelCase_		:       Optional[Any]				=      False
       UpperCamelCase_		:       Tuple				=      False
       UpperCamelCase_		:       Optional[int]				=      False
       UpperCamelCase_		:       List[Any]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=UpperCAmelCase_							,   hidden_size=37      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Any:







              """simple docstring"""
              self.config_tester.run_common_tests()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	List[Any]:







              """simple docstring"""
              # This regression test was failing with PyTorch < 1.3
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_decoder()

              UpperCamelCase       			=		None

              self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase_      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		"abeja/gpt-neox-japanese-2.7b"

              UpperCamelCase       			=		["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

              UpperCamelCase       			=		[
                  "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
                  "100年後に必要とされる会社は、「人」が中心の会社です。",
                  "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
                  "国境の長いトンネルを抜けると、そこは雪国だった。",
                  "美味しい日本食といえば、やっぱりお寿司ですよね。",
              ]

              UpperCamelCase       			=		GPTNeoXJapaneseTokenizer.from_pretrained(UpperCAmelCase_      )
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCAmelCase_      )

              UpperCamelCase       			=		[]
              for prompt in prompts:
                     UpperCamelCase       			=		tokenizer(UpperCAmelCase_							,   return_tensors="pt"      ).input_ids
                     UpperCamelCase       			=		model.generate(UpperCAmelCase_							,   max_length=50      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     predicted_outputs += generated_string
              self.assertListEqual(UpperCAmelCase_							,   UpperCAmelCase_      )





